With the continuous social progress, the application range of the video monitoring system becomes increasingly wider. Surveillance cameras are installed usually at the entrances and exits of the places, such as the super markets, markets, stadiums, and airports, stations, and the like, to monitor the entrances and exits of these places by the security personnel and managers. On the other hand, the people flow at the entrances and exits of the places, such as the super markets, markets, stadiums, and airports, stations, and the like, is very important to the operators and managers of the above places, wherein, the people flow is the number of the people flowing to a certain direction, in the present document, it indicates specifically the number of the people flowing to the entry direction and exit direction both.
As to the prior video monitoring, the people flow statistics is primarily implemented by the monitoring personnel with manual inventory. Such method for counting manually the people flow is reliable under the condition that the monitoring time period is short and the people flow is sparse, however, because of the limitations of the biological characteristics of the human eye, under the condition that the monitoring time period is long and the people flow is dense, the accuracy of the statistics will be decreased significantly, and the manual statistics method will spend a lot of labor costs. Automatic inventory of the people flow can be realized by the people flow statistics method based on the video analyzing, and various problems produced by the manual statistics can be solved. Currently, there are mainly three types of flow statistics methods based on the video analyzing:
The first one is a method based on tracking the feature points, in said method, some moving feature points are tracked firstly, then the tracks of the feature points are made cluster analysis, thereby the people flow information can be obtained; in the method based on tracking the feature points, some moving feature points are required to be tracked, then the tracks of the feature points are made cluster analysis, thereby the people flow information can be obtained, the disadvantage of said method is that it is difficult to track stably the feature points themselves, so the counting accuracy is not very good.
The second one is a method based on human body segmentation and tracking, in said method, it is required to extract firstly a moving target block, then the moving target block is segmented to obtain single human body targets, and finally, the respective single human body targets are tracked to realize the people flow statistics; in the method based on the human body segmentation and tracking, the target blocks which are moving will be extracted firstly, then single human body targets can be obtained by segmenting the moving target block, at last, they are tracked to obtained the respective human body tracks, thereby the people flow statistics can be realized. The disadvantage of said method is that the accuracy of the human body segmentation cannot be ensured when the human body is shielded, and this affects the statistical accuracy.
The third one is a method based on detecting and tracking the human head or head and shoulder, in said method, human head or head and shoulder are detected from the video, and the people flow statistics is performed by tracking the human head or head and shoulder. The method based on detecting and tracking the human head detects the human heads from the video, the people flow statistics is performed by tracking the human heads, when the angle of the camera is suitable, there is less condition that the human heads are shielded, thereby the accuracy of the method based on detecting the human heads can be improved comparing with the above two methods, currently, the method for counting the number of people based on human head detection has been suggested by some companies, for example, in the method of the patent document of application number 200910076256.X, suggested by Vimicro, Beijing, firstly, a moving foreground is extracted, then two serial classifiers are trained by using haar characteristics, and the human heads with predetermined size are searched in the foreground to realize the human head detection, wherein the haar characteristic is a rectangle characteristic, the shape and gray level information of the target can be described by changing the size and the combination method of the rectangle. The classifiers used in said method only detect the targets of the same type, and they cannot detect different types of targets simultaneously, for example, they cannot detect the human head of dark colored hair (including wearing dark colored cap) and the human head of light colored hair (including wearing light colored cap) simultaneously, and it causes that the human head statistics is not comprehensive.